A Scaffold-Assisted 3D Cancer Cell Model for Surface-Enhanced Raman Scattering-Based Real-Time Sensing and Imaging

Despite recent advances in the development of scaffold-based three-dimensional (3D) cell models, challenges persist in imaging and monitoring cell behavior within these complex structures due to their heterogeneous cell distribution and geometries. Incorporating sensors into 3D scaffolds provides a potential solution for real-time, in situ sensing and imaging of biological processes such as cell growth and disease development. We introduce a 3D printed hydrogel-based scaffold capable of supporting both surface-enhanced Raman scattering (SERS) biosensing and imaging of 3D breast cancer cell models. The scaffold incorporates plasmonic nanoparticles and SERS tags, for sensing and imaging, respectively. We demonstrate the scaffold's adaptability and modularity in supporting breast cancer spheroids, thereby enabling spatial and temporal monitoring of tumor evolution.

SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors

The tumor microenvironment (TME) is a dynamic pseudoorgan that shapes the development and progression of cancers. It is a complex ecosystem shaped by interactions between tumor and stromal cells. Although the traditional focus has been on the paracrine communication mediated by protein messengers, recent attention has turned to the metabolic secretome in tumors. Metabolic enzymes, together with exchanged substrates and products, have emerged as potential biomarkers and therapeutic targets. However, traditional techniques for profiling secreted metabolites in complex cellular contexts are limited. Surface-enhanced Raman scattering (SERS) has emerged as a promising alternative due to its nontargeted nature and simplicity of operation. Although SERS has demonstrated its potential for detecting metabolites in biological settings, its application in deciphering metabolic interactions within multicellular systems like the TME remains underexplored. In this study, we introduce a SERS-based strategy to investigate the secreted purine metabolites of tumor cells lacking methylthioadenosine phosphorylase (MTAP), a common genetic event associated with poor prognosis in various cancers. Our SERS analysis reveals that MTAP-deficient cancer cells selectively produce methylthioadenosine (MTA), which is taken up and metabolized by fibroblasts. Fibroblasts exposed to MTA exhibit: i) molecular reprogramming compatible with cancer aggressiveness, ii) a significant production of purine derivatives that could be readily recycled by cancer cells, and iii) the capacity to secrete purine derivatives that induce macrophage polarization. Our study supports the potential of SERS for cancer metabolism research and reveals an unprecedented paracrine crosstalk that explains TME reprogramming in MTAP-deleted cancers.

Machine Learning-Assisted High-Throughput SERS Classification of Cell Secretomes

During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.

Generalization of Self-Assembly Toward Differently Shaped Colloidal Nanoparticles for Plasmonic Superlattices

Periodic superlattices of noble metal nanoparticles  have demonstrated superior plasmonic properties compared to randomly distributed plasmonic arrangements due to near-field coupling and constructive far-field interference. Here, a chemically driven, templated self-assembly process of colloidal gold nanoparticles is investigated and optimized, and the technology is extended toward a generalized assembly process for variously shaped particles, such as spheres, rods, and triangles. The process yields periodic superlattices of homogenous  nanoparticle clusters on a centimeter scale. Electromagnetically simulated absorption spectra and corresponding experimental extinction measurements demonstrate excellent agreement in the far-field for all particle types and different lattice periods. The electromagnetic simulations reveal the specific nano-cluster near-field behavior, predicting the experimental findings provided by surface-enhanced Raman scattering measurements. It turns out that periodic arrays of spherical nanoparticles produce higher surface-enhanced Raman scattering enhancement factors than particles with less symmetry as a result of very well-defined strong hotspots.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Nanocomposite Scaffolds for Monitoring of Drug Diffusion in Three-Dimensional Cell Environments by Surface-Enhanced Raman Spectroscopy

Monitoring dynamic processes in complex cellular environments requires the integration of uniformly distributed detectors within such three-dimensional (3D) networks, to an extent that the sensor could provide real-time information on nearby perturbations in a non-invasive manner. In this context, the development of 3D-printed structures that can function as both sensors and cell culture platforms emerges as a promising strategy, not only for mimicking a specific cell niche but also toward identifying its characteristic physicochemical conditions, such as concentration gradients. We present herein a 3D cancer model that incorporates a hydrogel-based scaffold containing gold nanorods. In addition to sustaining cell growth, the printed nanocomposite inks display the ability to uncover drug diffusion profiles by surface-enhanced Raman scattering, with high spatiotemporal resolution. We additionally demonstrate that the acquired information could pave the way to designing novel strategies for drug discovery in cancer therapy, through correlation of drug diffusion with cell death.

Mechanically Tunable Lattice-Plasmon Resonances by Templated Self-Assembled Superlattices for Multi-Wavelength Surface-Enhanced Raman Spectroscopy

Lattice plasmons, i.e., diffractively coupled localized surface plasmon resonances, occur in long-range ordered plasmonic nanostructures such as 1D and 2D periodic lattices. Such far-field coupled resonances can be employed for ultrasensitive surface-enhanced Raman spectroscopy (SERS), provided they are spectrally matched to the excitation wavelength. The spectral positions of lattice plasmon modes critically depend on the lattice period and uniformity, owing to their pronounced sensitivity to structural disorder. We report the fabrication of superlattices by templated self-assembly of gold nanoparticles on a flexible support, with tunable lattice-plasmon resonances by means of macroscopic strain. We demonstrate that the highest SERS performance is achieved by matching the lattice plasmon mode to the excitation wavelength, by post-assembly fine-tuning of long-range structural parameters. Both asymmetric and symmetric lattice deformations can be used to adapt a single lattice structure to both red-shifted and blue-shifted excitation lines, as exemplified by lattice expansion and contraction, respectively. This proof-of-principle study represents a basis for alternative designs of adaptive functional nanostructures with mechanically tunable lattice resonances using strain as a macroscopic control parameter.

SERS monitoring of local pH in encapsulated therapeutic cells

Microencapsulation of therapeutic cells has widely advanced toward the development of treatments for various diseases, in particular seeking the protection of cell transplants from immune rejection. However, several challenges in cell therapy remain due to the lack of suitable methods to monitor in vivo microcapsule tracking, microcapsule stability and/or altered cell viability and proliferation upon transplantation. We propose in this work the incorporation of contrast agents in microcapsules, which can be easily visualized by SERS imaging. By placing SERS probes in the alginate extracellular layer, a high contrast can be obtained with negligible toxicity. Specifically, we used a pH-sensitive SERS tracking probe consisting of gold nanostars encoded with a pH-sensitive Raman-active molecule, and protected by a layer of biocompatible polymer coating, grafted on the nanoparticles via electrostatic interactions. This nanomaterial is highly sensitive within the biologically relevant pH range, 5.5-7.8. We demonstrate that this SERS-based pH sensor can provide information about cell death of microencapsulated cells, in a non-invasive manner. As a result, we expect that this approach should provide a general strategy to study biological interactions at the microcapsule level.

Preventing Memory Effects in Surface-Enhanced Raman Scattering Substrates by Polymer Coating and Laser-Activated Deprotection

The development of continuous monitoring systems requires in situ sensors that are capable of screening multiple chemical species and providing real-time information. Such in situ measurements, in which the sample is analyzed at the point of interest, are hindered by underlying problems derived from the recording of successive measurements within complex environments. In this context, surface-enhanced Raman scattering (SERS) spectroscopy appears as a noninvasive technology with the ability of identifying low concentrations of chemical species as well as resolving dynamic processes under different conditions. To this aim, the technique requires the use of a plasmonic substrate, typically made of nanostructured metals such as gold or silver, to enhance the Raman signal of adsorbed molecules (the analyte). However, a common source of uncertainty in real-time SERS measurements originates from the irreversible adsorption of (analyte) molecules onto the plasmonic substrate, which may interfere in subsequent measurements. This so-called "SERS memory effect" leads to measurements that do not accurately reflect varying conditions of the sample over time. We introduce herein the design of plasmonic substrates involving a nonpermeable poly(lactic-co-glycolic acid) (PLGA) thin layer on top of the plasmonic nanostructure, toward controlling the adsorption of molecules at different times. The polymeric layer can be locally degraded by irradiation with the same laser used for SERS measurements (albeit at a higher fluence), thereby creating a micrometer-sized window on the plasmonic substrate available to molecules present in solution at a selected measurement time. Using SERS substrates coated with such thermolabile polymer layers, we demonstrate the possibility of performing over 10,000 consecutive measurements per substrate as well as accurate continuous monitoring of analytes in microfluidic channels and biological systems.

From individual to collective 3D cancer dissemination: roles of collagen concentration and TGF-β

Cancer cells have the ability to migrate from the primary (original) site to other places in the body. The extracellular matrix affects cancer cell migratory capacity and has been correlated with tissue-specific spreading patterns. However, how the matrix orchestrates these behaviors remains unclear. Here, we investigated how both higher collagen concentrations and TGF-β regulate the formation of H1299 cell (a non-small cell lung cancer cell line) spheroids within 3D collagen-based matrices and promote cancer cell invasive capacity. We show that at low collagen concentrations, tumor cells move individually and have moderate invasive capacity, whereas when the collagen concentration is increased, the formation of cell clusters is promoted. In addition, when the concentration of TGF-β in the microenvironment is lower, most of the clusters are aggregates of cancer cells with a spheroid-like morphology and poor migratory capacity. In contrast, higher concentrations of TGF-β induced the formation of clusters with a notably higher invasive capacity, resulting in clear strand-like collective cell migration. Our results show that the concentration of the extracellular matrix is a key regulator of the formation of tumor clusters that affects their development and growth. In addition, chemical factors create a microenvironment that promotes the transformation of idle tumor clusters into very active, invasive tumor structures. These results collectively demonstrate the relevant regulatory role of the mechano-chemical microenvironment in leading the preferential metastasis of tumor cells to specific tissues with high collagen concentrations and TFG-β activity.